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Critical-grade insults (CGIs), such as sepsis, pose extreme challenges to host homeostasis. Although unfavorable immune responses in these conditions are commonly described as dysregulated, such perturbations are often defined retrospectively based on clinical outcomes rather than on the underlying immune processes. Here, I propose that, particularly in its early phase, the immune system response to sepsis reflects a high-stakes host response aimed at rapid control of the insult, albeit one that carries substantial risk to the host. If the inciting challenge is effectively contained, the immune response may resolve and transition toward tissue repair and recovery. Conversely, failure to control the insult may result in host death. In this context, the early immune response can be conceptualized within a “win versus loss” framework, where win is described as the ability to control inciting insult, while loss is host demise. Consequently, the term “dysregulation” may not fully capture the probabilistic feature of the initial response. Rather than representing a simple failure of control, this response may reflect an aggressive but potentially hazardous strategy that, in some cases, proves unsuccessful. Timely medical interventions, including appropriate antibiotic therapy, source control, or immunological endotype-based strategies, can modify CGI immunological trajectory by limiting insult burden and reducing the duration and intensity of the host response, thereby improving the likelihood of survival. Nevertheless, sustained or excessive early immune activation may disrupt regulatory networks, resulting in inevitable demise in some individuals, while others will exhibit an emergence of maladaptive allostatic states that can impair full recovery.

Abstract Background High‐resolution MRI is essential for accurate diagnosis and treatment planning, but its clinical acquisition is often constrained by long scanning times, which increase patient discomfort and reduce scanner throughput. While super‐resolution (SR) techniques offer a post‐acquisition solution to enhance resolution, existing deep learning approaches face trade‐offs between reconstruction fidelity and computational efficiency, limiting their clinical applicability. Purpose This study aims to develop an efficient and accurate deep learning framework for MRI SR that preserves fine anatomical detail while maintaining low computational overhead, enabling practical integration into clinical workflows. Materials and Methods We propose a novel SR framework based on multi‐head selective state‐space models (MHSSM) integrated with a lightweight channel multilayer perceptron (MLP). The model employs 2D patch extraction with hybrid scanning strategies (vertical, horizontal, and diagonal) to capture long‐range dependencies while mitigating pixel forgetting. Each MambaFormer block combines MHSSM, depthwise convolutions, and gated channel mixing to balance local and global feature representation. The framework was trained and evaluated on two distinct datasets: 7T brain T1 MP2RAGE maps (142 subjects) and 1.5T prostate T2w MRI (334 subjects). Performance was compared against multiple baselines including Bicubic interpolation, GAN‐based (CycleGAN, Pix2pix, SPSR), transformer‐based (SwinIR), Mamba‐based (MambaIR), and diffusion‐based (I 2 SB, Res‐SRDiff) methods. Results The proposed model demonstrated superior performance across all evaluation metrics while maintaining exceptional computational efficiency. On the 7T brain dataset, our method achieved the highest structural similarity (SSIM: ) and peak signal‐to‐noise ratio (PSNR: dB), along with the best perceptual quality scores (LPIPS: ; GMSD: ). These results represented statistically significant improvements over all baselines (), including a 2.1% SSIM gain over SPSR and a 2.4% PSNR improvement over Res‐SRDiff. For the prostate dataset, the model similarly outperformed competing approaches, achieving SSIM of , PSNR of dB, LPIPS of , and GMSD of . Notably, our framework accomplished these results with only 0.9 million parameters and 57 GFLOPs, representing reductions of 99.8% in parameters and 97.5% in computational operations compared to Res‐SRDiff, while also substantially outperforming SwinIR and MambaIR in both accuracy and efficiency metrics. Conclusion The proposed framework provides a computationally efficient yet accurate solution for MRI SR, delivering well‐defined anatomical details and improved perceptual fidelity across anatomically distinct datasets. By significantly reducing computational demands while maintaining state‐of‐the‐art performance, the model offers strong potential for feasibility toward clinical translation and scalable integration into future imaging workflows.

Context.— In 2016, the College of American Pathologists published an evidence-based guideline to develop recommendations for interpretive diagnostic error reduction in anatomic pathology. New evidence, together with evolving practice environments and the continued adoption of digital technologies, including artificial intelligence, has prompted a review of the guideline. Objective.— To assess evidence published since the release of the original guideline and update evidence-based recommendations for the reduction of diagnostic errors. Design.— The College of American Pathologists, in collaboration with the Association of Directors of Anatomic and Subspecialty Pathology, convened a panel of experts to update the 2016 evidence-based guideline. A systematic literature review was conducted to address 4 key questions. Using the Grading of Recommendations Assessment, Development and Evaluation approach, recommendations were created based on the available evidence, certainty of that evidence, and key judgments as defined in the Grading of Recommendations Assessment, Development and Evaluation framework. Results.— Two strong recommendations and 4 good practice statements were established for the reduction of diagnostic error. Conclusions.— The expert panel recommends that anatomic pathologists implement procedures for the review of pathology cases to detect and correct potential interpretive errors to improve patient outcomes.

ABSTRACT Early identification of patients with treatment‐responsive rapidly progressive dementia (RPD) is important as early treatment improves outcomes. The STAM 3 P score identifies treatment‐responsive RPD using “high risk” presenting features. We optimized performance by adding a time component (i.e., dementia within 3 months) and validated the modified “STAM3 m P score” in two independent cohorts ( n = 302). A STAM3 m P score ≥ 1 identified 96% of patients with treatment‐responsive RPD; ≥ 3 features were highly specific (98%). These data suggest that ≥ 1 STAM3 m P features effectively identifies treatment‐responsive RPD and may be used to prioritize investigations, whereas ≥ 3 STAM3 m P features are highly specific and may inform early treatment decisions.

ABSTRACT Introduction Community health workers (CHWs) help patients navigate community resources. CHW programs can improve health outcomes and reduce healthcare utilization, but identifying eligible patients is challenging. We developed an electronic health record (EHR)‐based algorithm trained to predict referrals to a CHW program. Our objective was to evaluate whether an algorithm trained on historical referrals identifies a subgroup of patients that differs from the subgroup identified through questionnaire‐based health‐related social needs (HRSN) screening alone. Methods This analysis used data from Mayo Clinic primary care patients in Southeast Minnesota. We developed a gradient boosted time‐to‐event model that incorporated demographics, diagnoses, HRSN questionnaire responses, and HRSNs derived from clinical notes using natural language processing. We compared the characteristics of algorithm‐identified patients with those identified through questionnaires and assessed the variable importance of model features. Results The algorithm had AUC: 0.87 in the training and 0.92 in the hold out set. Patients flagged by the algorithm tended to use interpreter services more commonly (12.6% vs. 1.3%), have a non‐English preferred language (14.1% vs. 2.3%), and have more instances of health literacy documented (5.36 vs. 1.29) compared to patients identified by the HRSN questionnaire. Only 47.0% of patients flagged by the algorithm had responded to the HRSN questionnaire within 2 years. Variable importance was highest for health literacy and financial strain from unstructured notes. Conclusions The algorithm is a chart‐review prioritization tool for referral to the CHW program for patients that may overcome challenges to identifying patients with HRSNs, especially when questionnaire data is incomplete.

e12601 Background: Mammography remains the standard modality for breast cancer (BC) screening but has several limitations. There is a critical need for minimally invasive markers that can aid in distinguishing benign, non-invasive lesions from those likely to progress to invasive BC. Platelets are nature’s transport mechanism for delivery of growth factors and inhibitors. Assaying the platelet proteome, Hessian Labs developed HeLP (Hessian Labs Personalized) test designed to identify early malignancy, as well as its grade, and invasiveness. The test is based on a discovery that platelets sequester oncogenic regulators against a concentration gradient in plasma early in tumor development. Hence, it has the potential to differentiate benign from invasive BC at various stages, stratifying the need for intervention. Methods: Platelet samples from 120 women with BC were analyzed using a discovery data-independent acquisition mass spectrometry (DIA-MS). The identified differentially expressed platelet-associated proteins, were prioritized using ClariaIQ , a proprietary computational platform that ranks proteins based on their relevance to stage-specific oncogenic pathways and established Cancer Hallmarks, to identify stage- and grade-associated proteomic signatures and create the HeLP panel. Results: DIA-MS identified multiple platelet sequestered proteins that were differentially expressed between indolent disease and invasive BC. ClariaIQ prioritization resulted in the selection of 13 proteins for inclusion in the HeLP breast cancer assay. We present two examples: SAA1 and SERPIN proteins demonstrated differential expression between healthy, benign breast lesions and malignant invasive disease (Table 1). SAA1 peaks in early BC and SERPIN in advanced stages. With SAA1, in HER negative BC the levels rise to an average of 285,000 RE units when compared to HER2 positive BC. Conclusions: Platelet sequestered protein signatures provide clinically relevant information regarding breast cancer status, grade, and invasiveness. The findings support the robustness of the signal, and a more patient-specific signature than traditional plasma or serum biomarkers. It suggests a role for HeLP as a minimally invasive adjunct to mammography to improve diagnostic stratification and therapeutic decision-making. Disease Group SAA1 Mean (RE) SAA1 SD (RE) SERPIN A3 Mean (RE) SERPIN A3 SD (RE) Healthy Controls 119,592 206,349 490,231 279,017 Benign Breast Lesions 622,187 50,855 341,102 153,144 Stage I Breast Cancer 1,223,865 1,878,654 742,409 466,468 Stage II Breast Cancer 663,369 1,970,265 922,381 844,512 Stage III Breast Cancer 437,853 662,211 705,994 597,399 Stage IV Breast Cancer 225,265 384,826 1,068,874 1,415,934 RE=Relative Expression units. N=20 subjects included in each group.

9557 Background: High-dose interleukin-2 (IL-2) is administered following tumor-infiltrating lymphocyte (TIL) therapy, yet cardiotoxicity frequently limits dose delivery. The clinical significance of peri-dose troponin elevations during IL-2 remains poorly defined. We evaluated troponin kinetics during IL-2 dosing and their association with significant cardiac events and IL-2 interruption. Methods: We retrospectively analyzed patients with metastatic melanoma treated with TIL therapy followed by inpatient high-dose IL-2 at Mayo Clinic Rochester (N = 24; 87 IL-2 doses). High-sensitivity troponin and ECGs were obtained before and after each IL-2 dose. Clinically significant cardiac events were defined as QTc prolongation > 480 ms, arrhythmias (excluding isolated sinus tachycardia), new ECG abnormalities, vasopressor requirement, or cardiology consultation. Dose-level outcomes included cardiac events and subsequent IL-2 dose interruption. Paired troponin changes were assessed using Wilcoxon signed-rank testing. Dose-level associations were evaluated using clustered logistic regression to account for repeated IL-2 doses within patients. Results: 24 patients received 87 IL-2 doses (median 3 [IQR 2 - 5]). Median age was 63 years (IQR 54 - 70), and 58% were male. Baseline troponin median was 11 ng/L (IQR 6 - 21); 38% had baseline troponin > 14 ng/L. Post-dose troponin exceeded 14 ng/L in 16/24 patients (67%) and 48/87 doses (52%). Median delta troponin was 0 ng/L (IQR −1 to 6; range −24 to 133), with post-dose levels higher than pre-dose values ( p = 0.02). Clinically significant cardiac events occurred in 33% of patients; no post-IL-2 reductions in left ventricular ejection fraction were observed. At the dose level, cardiac events occurred only when post-dose troponin exceeded 14 ng/L (15/48 [31%] vs 0/39 doses; p < 0.001). In clustered logistic regression, post-dose troponin ≥15 ng/L was independently associated with cardiac events (OR 9.6, 95% CI 1.5 - 60.6; p = 0.02) and subsequent IL-2 dose interruption (OR 3.4, 95% CI 1.1 - 10.7; p = 0.04). When modeled continuously, each 1-unit increase in log-transformed post-dose troponin was associated with higher odds of cardiac events (OR 3.3, 95% CI 1.6 - 6.7; p = 0.001) and IL-2 interruption (OR 2.8, 95% CI 1.6 - 4.9; p < 0.001). Baseline troponin correlated with peak post-dose troponin (ρ = 0.44, p = 0.04). Conclusions: Peri-dose troponin elevations are common during post-TIL high-dose IL-2; however, higher post-dose troponin independently identifies clinically significant cardiac toxicity and predicts IL-2 interruption after accounting for repeated dosing within patients. Baseline troponin modestly predicts peak elevations, supporting standardized, risk-stratified troponin monitoring and prospective evaluation of toxicity-adaptive IL-2 delivery strategies in TIL therapy.

9573 Background: Clinical Stage III (cSIII) melanoma patients are at high risk of recurrence following therapeutic lymph node dissection (TLND) and adjuvant therapy. Neoadjuvant immunotherapy based regimens have shown promising results in this setting, with pathological response being the best predictor of recurrence free survival (RFS) and distant metastases free survival (DMFS). T-cell Receptor (TCR) abundance and clonality are being explored as biomarkers. Methods: In the phase II NeoACTIVATE (NCT03554083) trial, patients were treated based on BRAF status with 12 weeks of neoadjuvant atezolizumab, vemurafenib and cobimetinib (Arm A, BRAF-mutated) or atezolizumab and cobimetinib (Arm B, BRAF wildtype), followed by TLND and 6 months adjuvant atezolizumab. TCR sequencing (n=23) was performed on the pretreatment involved lymph node (iLN) and on the same node, along with an adjacent uninvolved node (uiLN) after TLND. The fraction productive of T cells (fpTC) was defined as the percentage of in-frame TCR sequences (capable of encoding a functional expressed receptor protein) of all TCR sequences identified. Simpson Clonality Index (SCI) was used to measure relative abundance of specific clones, with a higher score indicating a more monoclonal repertoire. Comparisons were made using the Wilcoxon Rank Sum test. Results: 30 patients were enrolled, 15 in each arm. 2 patients were not operated on, and one did not receive adjuvant treatment, leaving 27 patients for outcome analysis. 3-year RFS was 55.6% (95% CI 9.5%-77.8%), median of 62.2 months (95% CI 19-NR); DMFS was 64.3% (95% CI 48%-86.3%), median 62.2 months (95% CI 33.9-NR). The fpTC was higher in the pretreatment iLN for those without an RFS event (p=0.04) with a trend in the uiLN as well (p=0.06). Patients without a DMFS event had a higher fpTC in the uiLN (p=0.03) but not in the iLN. The SCI was significantly higher only in the post treatment iLN for patients without an RFS event (p=0.03) and was not significantly different when compared based on DMFS status. When performing pairwise comparisons of nodes based on recurrence status, we found that patients who did not suffer an RFS event had a significantly higher SCI in the post treatment iLN compared to the uiLN (p=0.02). Conclusions: Neoadjuvant immunotherapy and targeted therapy for cSIII melanoma can lead to durable RFS. Higher pretreatment fpTC as well as higher post-treatment iLN TCR clonality were both associated with improved RFS. These data indicate that TCR repertoire characteristics within iLN and uiLN may reflect differential immune responses to neoadjuvant therapy and could inform future studies evaluating biomarkers to stratify risk of recurrence and allow personalization of therapy. Clinical trial information: NCT03554083 . fpTC Simpson Clonality Index RFS event DMFS event RFS event DMFS event Pretreatment iLN 0.04 0.12 0.43 0.11 Posttreatment iLN 0.09 0.09 0.03 0.12 uiLN 0.06 0.03 0.79 0.48 Δ iLN (post-pre) 0.79 0.37 0.07 0.41 Δ posttreatment iLN – uiLN 0.21 0.21 0.02 0.08

9558 Background: Ipilimumab 3 mg/kg plus nivolumab 1 mg/kg (Ipi3+Nivo1) is the standard immunotherapy regimen for metastatic melanoma with brain metastases (MBM) but is associated with significant immune-related toxicity, particularly neurologic toxicity. Ipilimumab 1 mg/kg plus nivolumab 3 mg/kg (Ipi1+Nivo3) has demonstrated improved tolerability in extracranial melanoma, though comparative data in MBM are limited. We compared efficacy and toxicity of these dosing strategies in a multisite real-world cohort. Methods: We retrospectively identified patients with MBM treated with ipilimumab plus nivolumab across three Mayo Clinic sites between 2016 and 2024. Patients were classified by induction dosing as Ipi1+Nivo3 or Ipi3+Nivo1. Overall survival (OS) and progression-free survival (PFS) were estimated using the Kaplan–Meier method and compared using log-rank tests. Best overall response (BOR) and toxicity outcomes were summarized descriptively. Results: Among 147 patients (Ipi1+Nivo3 n=32; Ipi3+Nivo1 n=115), 144 were evaluable for survival. Median age was higher in the Ipi1+Nivo3 group (69.5 vs 66.0 years); males predominated in both groups (71.9% vs 61.7%), and the cohort was predominantly White (>96%). Most patients had ECOG 0–1 in both groups (84.4% in Ipi1+ Nivo3 and 90.4% in Ipi3+Nivo1). BRAF mutation status was balanced between regimens, with approximately half of patients harboring BRAF-mutant disease (51.6% vs 53.2%). Median OS was 18.5 months (95% CI, 5.7–54.7) with Ipi1+Nivo3 versus 10.6 months (95% CI, 6.5–19.1) with Ipi3+Nivo1 (log-rank p=0.25). Two-year OS was 45.9% and 35.0%, and five-year OS was 24.5% and 19.2%, respectively. Median PFS was 7.3 months (95% CI, 1.8–10.9) versus 2.8 months (95% CI, 2.1–4.4) (log-rank p=0.16). BOR was available for 138 patients; objective response rates were 35.5% and 31.8%, respectively. 3–5 adverse events occurred in 24.1% of Ipi1+Nivo3 versus 30.5% of Ipi3+Nivo1 patients. Hospitalizations were less frequent (16.7% vs 36.1%), and neurologic adverse events occurred only with Ipi3+Nivo1 (11.9% vs 0%). Conclusions: In this multisite real-world MBM cohort, Ipi1+Nivo3 demonstrated comparable efficacy, numerically longer survival, and a more favorable toxicity profile compared with standard Ipi3+Nivo1. Notably, no neurologic toxicity was observed with the Ipi1+Nivo3 regimen. These findings support further prospective evaluation of lower dose ipilimumab strategies. Response and survival outcomes. Outcome Ipi1 + Nivo3 Ipi3 + Nivo1 Complete response (CR) 2 / 31 (6.5%) 8 / 107 (7.5%) Partial response (PR) 9 / 31 (29.0%) 26 / 107 (24.3%) Objective response rate (ORR = CR+PR) 11 / 31 (35.5%) 34 / 107 (31.8%) Disease control rate (DCR = CR+PR+SD) 18 / 31 (58.1%) 51 / 107 (47.7%) Median PFS, months (95% CI) 7.3 (1.8–10.9) 2.8 (2.1–4.4) 2-year PFS 27.6% 22.3% Median OS, months (95% CI) 18.5 (5.7–54.7) 10.6 (6.5–19.1) 2-year OS 45.9% 35.0% 5-year OS 24.5% 19.2%